We then apply a smoothing kernel function \(K_h(s)\)  to adjust the similarity between the users. The kernel is a symmetric uni-modal function, returning a non-negative weight when the distance is within a bandwidth \(h\) ; it covers a broader range with larger \(h\)  and a narrower range with smaller \(h\) . (In our experiment, we consider the Epanechnikov kernel function as in [1]}.)
